This study explores the differences between crowd-shipping users and non-users based on responses to 2016 online. We use proportional t-test analysis and a binary logit model to study how and to what extent the attitudes, preferences, and characteristics of crowd-shipping users differ from non-users. The results show that (1) crowd-shipping is more prevalent among young people, men, and full-time employed individuals, (2) urban areas are preferential for the development of crowd-shipping, and (3) crowd-shipping users are most inclined to use the system for medium-distance deliveries. The elasticity analysis indicates that individuals who have a strong sense of community and environmental concern are, respectively, 86.4% and 83.9% more likely to use crowd-shipping. However, individuals who have reservations regarding affordability and trust are 68.3% and 64.9% less likely to use crowd-shipping, respectively. The sensitivity analysis reveals these magnitudes of effects to vary among different population segments with experience in sending packages and gender being the most sensitive strata. The findings aid our understanding of the interaction of emerging shipping systems and user dynamics by providing a pioneering investigation of the determinants of crowd-shipping use.

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Community mobility MAUP-ing: A Socio-Spatial Investigation of Bikeshare Demand in Chicago

The expansion and evolution of bikesharing systems is a global phenomenon, which has motivated research to characterize “best practices” in both system operations and policy transferability across regions. Little is known, however, about the pros and cons of different approaches to define scale and zoning schemes in bikesharing evaluation. This research begins to address this challenge by juxtaposing station-level and community-level approaches to model and estimate the Annual Average Daily Bicyclist (AADB). We use the demand information from 459 Divvy stations in Chicago between June 1, 2015 and May 31, 2016. We assess the aggregation approaches concerning variable impacts, model specification, and prediction accuracy. Elasticity calculations, prediction error comparisons, and influence analysis reveal the importance of both built environment and sociodemographic variables in bikeshare modeling and the need to develop context-sensitive interventions. The detailed comparison of different levels of aggregation for analysis of bikeshare demand and user impact highlights that each level contributes insights to planners and policymakers. While disaggregate data provides the most information for planners in terms of improving bikeshare systems, there is value in adopting an aggregated approach for transport policy that accounts for potential neighborhood effects. In addition, the control for socio-demographic factors around stations highlight the variation in socio-spatial effects that planners need to account for when measuring outcomes and equity impacts.

Crowdshipping is a frontier in logistics systems designed to allow citizens to connect via online platforms and organize goods delivery along planned travel routes. The goal of this paper is to highlight the factors that influence the acceptability and preferences for crowdshipping. Through a survey using stated choice scenarios discrete choice models controlling for context and experience effects are specified. The results suggest that distinct preference patterns exist for distance classes of the shipment. In the local delivery setting, senders value transparency of driver performance monitoring along with speed, while longer shipments prioritize delivery conditions and driver training and experience. The model developed in this paper provides first key insights into the factors affecting preferences for goods delivery with occasional drivers.

Travel behaviour models typically rely on data afflicted by errors, both in perception (e.g., over/under-estimation by traveller) and measurement (e.g., software or researcher imputation error). Though such errors may have a relevant impact on model outputs, comprehensive frameworks dealing with different types of biases related to travel model inputs are scarce in the literature.

This paper focuses on the mitigation of errors typically occurring in travel time reporting in choice models. The aim is to explain the origin of these errors by including elements of travel behaviour (e.g., activities during the trip), which have been shown to significantly affect mode choices and commuting satisfaction. Using data from a revealed preference survey a hybrid choice model is estimated that treats travel time as a latent variable and incorporates different sources of data along with information on travel activities affecting the reported travel time measurement. Results from comparing a logit model assuming error-free inputs and the integrated hybrid model revealed significant impact on the generated policy outputs. The model results also demonstrate the main travel activity features that affects travel time reporting, providing indications to the mechanisms that can assist in understanding and mitigating the impact of imprecise measures.

User perceptions of service quality are essential to promote public transport ridership and trigger positive externalities. Therefore, research efforts need to analyze service quality from the point of view of users. This article builds on the stream of works studying perceptions of public transport service quality but shifts the focus towards user heterogeneity. Using a discrete choice experiment this article attempts to disentangle different dimensions of decision heterogeneity for bus services. Among the main findings the article discusses the implications of different types of decision heterogeneity, such as non-linear preferences, and relates this to the formulation of bus service contracts.

This report centers on identifying theories and methodological concepts to model innovation adoption in transportation systems. The focus was to summarize theory and model applications concerning penetration of new technologies among users, adaptation of choice patterns and attitude evolution over time. A second goal was to examine behavior measurement and data-collection. New development of serious immersive games as a method to assess dynamics in complex behavior arenas such as ride-sharing or driver cooperation was discussed.

The goal of the study is to measure the potential willingness of individuals to change status from pure commuters to traveler-shippers. In particular, it quantifies a potential crowdsourced shippers’ value of free time, or willingness-to-work (WTW), in the hypothetical scenario where crowdsourced shipping jobs are available in a variety of settings. This WTW calculation is unique compared to the traditional willingness-to-pay (WTP) in that it measures the tradeoff of making a profit and giving up time, instead of spending money to save time. This work provides a foundation to analyze the application and effectiveness of crowdsourced shipping by exploring the WTW propensity of ordinary travelers.

Terrorists around the world have recently targeted public transport systems, affecting in particular air and rail passengers. Terrorist attacks have long been acknowledged as having significant impacts on travel behavior. The paper analyzes (i) the impact security issues have on travel behavior and mode choice for long-distance travel and (ii) the travelers’ perception for government's efforts to ensure traveler security. The results show that a nonnegligible portion of sample would be willing to give up traveling in response to an increase in antiterrorism alerts. Moreover, respondents had strong variability, both how different models were viewed, and across respondents, concerning security threats.

An increasing number of studies are concerned with the use of alternatives to random utility maximisation as a decision rule in choice models, with a particular emphasis on regret minimisation over the last few years. The initial focus was on revealing which paradigm fits best for a given dataset, while later studies have looked at variation in decision rules across respondents within a dataset. However, only limited effort has gone towards understanding the potential drivers of decision rules, i.e. what makes it more or less likely that the choices of a given respondent can be explained by a particular paradigm. The present paper puts forward the notion that unobserved character traits can be a key source of this type of heterogeneity and proposes to characterise these traits through a latent variable within a hybrid framework. In an empirical application on stated choice data, we make use of a mixed random utility-random regret structure, where the allocation to a given class is driven in part by a latent variable which at the same time explains respondents' stated satisfaction with their real world commute journey. Results reveal a linkage between the likely decision rule and the stated satisfaction with the real world commute conditions. Notably, the most regret-prone respondents in our sample are more likely to have aligned their real-life commute performance more closely with their aspirational values.

User perceptions of service quality are essential to promote public transport ridership and trigger positive externalities. Therefore, research efforts need to analyze service quality from the point of view of users. This article builds on the stream of works studying perceptions of public transport service quality but shifts the focus towards user heterogeneity. Using a discrete choice experiment this article attempts to disentangle different dimensions of decision heterogeneity for bus services. Among the main findings the article discusses the implications of different types of decision heterogeneity, such as non-linear preferences, and relates this to the formulation of bus service contracts.